Phenological responses have been used as one of the major indicators of climate change. The timing of flowering and fruiting, the return of migrant birds and insects from winter habitats are easily and often measured, and records going back decades or centuries sometimes exist. Most importantly, shifts in phenological indicators are some of the strongest connections between rising temperatures and biological and ecological responses (for example). There is plenty of evidence, for example, that some migrant bird species are returning to their breeding grounds earlier than ever. These migratory birds may be responding (via migration timing) to warming temperatures in several ways: there may be plasticity or flexibility in individual timing of migration which allows them to respond to changing temperature cues; or species may also show adaptation via changes in the frequency of individuals with different migratory timings (microevolution). In cases where migratory species are responding to climate change, distinguishing the mechanisms allowing them to do so is surprisingly hard. Early arrival of migratory bird species is often explained as being due to individual plasticity or flexibility in “choosing” the date of migration, but the majority of studies of this phenomenon include little or no information about individual behaviour, only changes in the mean date of arrival for the entire population.

For this reason, Gill et al. looked at individual, rather than average population, arrival dates for Icelandic black-tailed godwits in south Iceland. Icelandic black-tailed godwits (“godwits” for the sake of brevity) have shown significant advances in the last 20 years in the timing of their spring arrival to the shores of Iceland, and these advances appear to relate to increasing temperatures. The population has also been banded such that 1-2% can be individually identified and tracked throughout their migratory range. Although only adults (of unknown age) were banded at the start of the experiment in 1999, recently chicks have also been banded and released and so a wide range of demographic classes are included with the banded birds.

From Gill et al. 2013.

When Gill et al. looked at date of arrival across 14 years for each individual, their results were surprisingly clear and cohesive. As previously reported, the population mean date of arrival in South Iceland had advanced as much as 2 weeks. But, this advance is not reflected in individual timing of arrivals over that same period – if a bird tends to arrive on a given day, they will continue to arrive on approximately that day every year, independent of temperature conditions. Instead, the population trend appears to be driven entirely by birds born in recent years – young individuals (recently hatched) tend to have arrival dates much earlier than older individuals. At least for the godwits, population wide trends in migration dates are actually driven by only a subset of the individuals.

From Gill et al. (2013)

Often it is assumed that migratory birds are responding to warming temperatures on an individual level: individuals respond to changing cues, resulting in shifts in arrival date. This study suggests otherwise, and finds that the important mechanism is not individual plasticity or microevolution but rather related to demographic shifts in arrival time. As to why younger birds arrive earlier, it is not clear, but may relate to the observation that nest building and hatching dates are also advancing. It may be that natal conditions are important – the authors observed a variety of possibly inter-related changes such that hatching dates are advancing and chick sizes are increasing, and the suggestion that mortality rates of later arriving individuals may also be higher. "Environmentally induced advances in arrival dates of recruits could operate through: (i) carry-over effects of changing natal conditions, (ii) changing patterns of mortality of individuals with differing arrival times, or (iii) arrival times being initially determined by conditions in the year of recruitment and individuals repeating those timings thereafter."

These results make some predictions about which populations of migratory birds might have the most ability to respond to warming climate - most likely those with shorter migratory distances, shorter times to reproduction and shorter-lifespans (hence decreasing the lag-time required for the population to catch up to temperature). It may also have relevance for other non-bird species that also rely on careful timing between phenology and temperature. Correspondingly, it suggests limitations - if individual behaviour is so inflexible and constrained, our hopes that some species may respond to climate change with behavioural changes seem far to simplistic.

Community ecology is difficult in part because it is so multi-dimensional: communities include possibly hundreds of species present, and in addition the niches of each of those species are multi-dimensional. Functional or trait-based approaches to ecology in particular have been presented as a solution to this problem, since fewer traits (compared to the number of species) may be needed to capture or predict a community’s dynamics. But even functional ecology is multi-dimensional, and many traits are necessary to truly understand a given species or community. The question, when measuring traits to delineate a community is: how many traits are necessary to capture species’ responses to their biotic and/or abiotic environment? Too few and you limit your understanding, too many and your workload becomes unfeasible.

Plant communities in particular have been approached using a functional framework (they don't move, so trait measurements aren't so difficult), but the number and types of traits that are usually measured vary from study to study. Plant ecologists have defined functional groups for plants which are ecologically similar, identified particular (“functional”) traits as being important, including SLA, seed mass, or height, or taken a "more is more" approach to measurements. There are even approaches that capture several dimensions by identifying important axes (leaf-height-seed strategy, etc.). Which of these approaches is best is not clear. In a new review, Daniel Laughlin rather ambitiously attempts to answer how many (and which) traits plant ecologists should consider. He asks whether the multi-dimensional nature of ecological systems is a curse (there is too much complexity for us to ever capture), or a blessing (is there a limit on how much complexity actually matters for understanding these systems)? Can dimensionality help plant ecologists determine the number of traits they need to measure?

From Laughlin 2013. The various trait axes (related to plant organs) important for plant function.

Laughlin suggests that an optimal approach to dimensionality should consider each plant organ (root, leaves, height, figure above). Many of the traits regularly measured are correlated (for example, specific leaf area, leaf dry matter content, lifespan, mass-based maximum rate of photosynthesis, dark respiration rates, leaf nitrogen concentration, leaf phosphorus concentration are all interrelated), and so potentially redundant sources of information. However, there are measurements in the same organ that may provide additional information – leaf surface area provides different information than measures of the leaf economic spectrum – and so the solution is not simply measuring fewer traits per organ. Despite redundancy in the traits plant ecologists measure, it is important to recognize that dimensionality is very high in plant communities. Statistical methods are useful for reducing dimensionality (for example, principle coordinate analysis), but even when applied, Laughlin implies that authors often over-reduce trait data by retaining to only a few axes of information.

Using 3 very large plant species-trait datasets (with 16-67(!) trait measures), Laughlin applies a variety of statistical methods to explore effective dimensionality reduction. He then estimates the intrinsic dimensionality (i.e. the number of dimensions necessary to capture the majority of the information in community structure) for the three datasets (figure below). The results were surprisingly consistent for each data set – even when 67 possible plant traits were available, the median intrinsic number of dimensions was only 4-6. While this is a reasonably low number, it's worth noting that the number of dimensions analyzed in the original papers using those datasets were too low (2-3 only).

From Laughlin 2013. The intrinsic number of traits/dimensions
necessary to capture variation in community structure.

For Laughlin, this result shows that dimensionality is a blessing, not a curse. After all, it should allow ecologists to limit the number of trait measures they need to make, provided they choose those traits wisely. Once the number of traits measured exceeds 8, there appears to be diminishing returns. The caveat is that the traits that are important to measure might differ between ecosystems – what matters in a desert is different than what matters in a rainforest. As always, knowing your system is incredibly important. Regardless, the review ends on a highly optimistic note – that complexity and multi-dimensionality of plant communities might not limit us as much as we fear. And perhaps less work is necessary for your next experiment.

Monday, November 11, 2013

I've seen a number of articles recently that explore in different way the intersection of environment and ecology, conservation and human societies. In particular, Frontiers in Ecology and Evolution (the free ESA journal you are gifted as a member) has dedicated an entire issue to the question of climate impacts on humans and ecosystems, and the papers cover important topics relating to changing climate and its effects on biodiversity, ecosystem integrity and human societies. Economic predictions suggest costs from fires, drought, and rising sea levels: whether protecting ecosystems will preserve their function and so mediate these costs to humans and other organisms is explored in depth. Of course, scholarly papers can be impersonal, but another article about the struggles of Inuit in the north to adapt (or not) to changing ecosystems provides a smaller, more human look at climate, development, and cultural change. Another study predicts that for some cultures, climate change (and the resulting difficulties growing food, maintaining livelihoods, obtaining water and human health risks) may be too much for them to withstand.

Finally, a long-form story by Paul Voosen in The Chronicle of Higher Education asks "Who is conservation for?". While not a novel question, through interviews with Gretchen Daily and Michael Soule, Voosen does a thorough job of illuminating conservation biology in the context of real-world limitations and realities, historical precedents, ongoing tensions between new and old approaches to conservation, and economic development. In the end it asks what motivates conservation: do we conserve purely for the sake of biodiversity alone, for economic and functional benefits, for aesthetic reasons, for charismatic and at-risk species? As Voosen subtly hints in the article, if leading conservation biologists can't agree on the answer, will it ever be possible to be effective?

Thursday, November 7, 2013

Human activity has impacted ecosystems around the globe, and
the value of intact, functioning habitats is increasingly appreciated. One of
the most important management options to maintain or increase the amount of
functioning habitat is to restore destroyed, disturbed and degraded habitats.
However, there is much concern about how predictable restoration efforts are
and the management strategies that will maximize success. The reality that
systems may reach very different, alternative ecosystem states is a problem for
managers when they desire well defined outcomes. Thus the ability to understand
and predict how different factors affect restoration outcomes would be an
important development.

In the current issue of the Journal of Applied Ecology,
Grman and colleagues examine how different factors influence prairie restoration
outcomes –specifically the diversity and composition of the restored habitat.
They considered several management, historical and environmental factors. For
management, they compiled information on the type of planting, the diversity
and density of sown seeds and fire manipulation. For local environmental
variables, they considered different soil characteristics, shade levels, and
site area. The historical influences included land-use history, rainfall during
seed sowing and site age. Finally, they also considered the landscape context;
specifically what habitats surrounded the restoration site.

Grman and colleagues show that restoration outcomes are most
influenced by management decisions and site history. The density, composition
and diversity of sown seeds had the greatest impact on restoration outcomes. Species
richness was highest in sites sown with high diversity. High sowing density
resulted in high beta diversity among sites. Site history had significant
effects on non-sown diversity, but did not influence the diversity of sown
species. Site characteristics failed to predict local diversity, but they were
important for among site beta-diversity.

If success is measured in terms of species diversity, then
this work clearly shows that management decisions directly influence success.
Surprisingly, site characteristics had a minor influence on success, despite
conceptual and theoretical models that predict system sensitivity to abiotic
influences. This work reinforces the need to develop the best management
options for prairie restoration and that the influences of site history and
local conditions can be overcome by sowing decisions and site management.

Wednesday, November 6, 2013

Some of the most frequently used ecological concepts can be difficult to define. Sometimes this lack of clarity leads to a poor understanding and a weak base for further research. A great example is “community structure”, a concept frequently mentioned and rarely defined that probably changes a lot from use to use. The phrase “we’re interested in how communities are structured” is tossed around a lot, and I suppose an understood definition is that community structure encompasses the species that are present in a community and their abundances. Community structure may refer to both a very simple concept (the abundances of species present in a community) and a very complicated one, connecting as it does mechanisms and models, observational data, and statistical measures. As a result, the precise way that ecologists delineate community structure and quantify it is both varied and vague.

The connection between models, communitystructure and metrics.

In the literature, it seems that there are two ways of approaching “community structure”: bottom-up, in which community structure is a predicted outcome of theoretical models of different mechanisms, and top-down, in which community structure is measured in a relatively statistical or descriptive fashion. Both are valuable approaches: while statistical metrics often are interpreted as providing evidence for particular models or mechanisms, the reverse logic – that a model predicts particular results for a given metric – is rarely explicitly considered. Making connections between the model results and the descriptive metrics might actually be fairly difficult. Model predictions are often complex and multidimensional, predicting changes through time, growth rates, the combinations of species that can or cannot coexist (but only if assumptions hold), or particular relationships between measures like diversity, abundances, and range sizes. Metrics are necessarily confined to a few dimensions (or perhaps are ordination approaches), focus on straightforward observational measures like abundance and presence, and further include observational error (sampling, etc). Because community structure means something different to these two approaches, the connections between metrics and models are poorly explored. A theoretician might find it difficult to relate ordinations of communities with the typical predictions from a mathematical model (which might be something like growth rates in relation to changes in abundance), while someone collecting field data might feel that the data they can collect is difficult to relate to the predictions of models.

Part of the problem is that for a long time, the default focus was on what types of interactions structured communities (environment, competition, predation, mutualisms), and niches were assumed to be necessarily driving community structure. The type of measurements and metrics used reflected this search for niches (e.g. comparing environmental gradients with community structure). Many quantitative metrics may tell you something about how community structure relates to different variables (spatial, environment, biotic) and how much variation is still unexplained. The consideration that niches might not always be important eventually led ecologists to compare patterns in community structure to random, null, or neutral expectations. As a result, in the simplest cases the answers to questions about community structure and niches are binary – different from random (niches matter), or not. Looking for complex patterns predicted by models-for example, the relative contribution of niche based and neutral processes to community structure-is difficult using common metrics of community structure (although there are some papers that do a good job of this).

It is interesting that this problem of disconnection between theoretical models of community structure and community structure metrics received the most attention through criticisms of phylogenetic metrics of diversity. There, patterns of over- and under-dispersion were criticized for not being the necessary outcome from models of competition or environmental filtering (i.e. Mayfield and Levine 2010). While those criticisms were mostly fair, they are equally deserved in most studies of species diversity, where patterns in ordinations or beta-diversity are frequently used to infer mechanisms. In contrast, one of the best approaches thus far to integrating model predictions for community structure with metrics of community structure are null models. Though they differ greatly in ecological realism and complexity, null models suggest expected community structure or metric values if none of the expected processes are structuring a community.

One of the greatest failings of the top-down approach is that recognizing patterns outside of the expected, such as those that include stochasticity or a combination of different processes, or the effects of history, is nearly impossible. Models that can incorporate these complexities provide little suggestion of how the patterns we can easily record in communities might reflect complex structuring processes. Ecological research is limited by the poor connection between both top-down and bottom-up approaches and its vague definition of community structure. Patterns more complicated than those that the top-down approach searches for are likely being missed, while relations between models and metrics (or development of new metrics) aren’t considered often enough. One solution might be to more meaningfully define community structure, perhaps as the association (or lack thereof) between the combination of species present in a community and the combination of abiotic and/or biotic processes present. This association is generally compared to an association between species and processes that might arise from random effects alone. The difference is that structure shouldn’t be considered separately from the processes that produce it, and the connections should be explicitly rather than implicitly made.